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  1. Many applications can benefit from data that increases performance but is not required for correctness (commonly referred to as soft state). Examples include cached data from backend web servers and memoized computations in data analytics systems. Today's systems generally statically limit the amount of memory they use for storing soft state in order to prevent unbounded growth that could exhaust the server's memory. Static provisioning, however, makes it difficult to respond to shifts in application demand for soft state and can leave significant amounts of memory idle. Existing OS kernels can only spend idle memory on caching disk blocks—which may not have the most utility—because they do not provide the right abstractions to safely allow applications to store their own soft state. To effectively manage and dynamically scale soft state, we propose soft memory, an elastic virtual memory abstraction with unmap-and-reconstruct semantics that makes it possible for applications to use idle memory to store whatever soft state they choose while guaranteeing both safety and efficiency. We present Midas, a soft memory management system that contains (1) a runtime that is linked to each application to manage soft memory objects and (2) OS kernel support that coordinates soft memory allocation between applications to maximize their performance. Our experiments with four real-world applications show that Midas can efficiently and safely harvest idle memory to store applications' soft state, delivering near-optimal application performance and responding to extreme memory pressure without running out of memory. 
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    Free, publicly-accessible full text available April 16, 2025
  2. Free, publicly-accessible full text available November 30, 2024
  3. Remote memory techniques are gaining traction in datacenters because they can significantly improve memory utilization. A popular approach is to use kernel-level, page-based memory swapping to deliver remote memory as it is transparent, enabling existing applications to benefit without modifications. Unfortunately, current implementations suffer from high software overheads, resulting in significantly worse tail latency and throughput relative to local memory. Hermit is a redesigned swap system that overcomes this limitation through a novel technique called adaptive, feedback-directed asynchrony. It takes non-urgent but time-consuming operations (e.g., swap-out, cgroup charge, I/O deduplication, etc.) off the fault-handling path and executes them asynchronously. Different from prior work such as Fastswap, Hermit collects runtime feedback and uses it to direct how asynchrony should be performed—i.e., whether asynchronous operations should be enabled, the level of asynchrony, and how asynchronous operations should be scheduled. We implemented Hermit in Linux 5.14. An evaluation with a set of latency-critical applications shows that Hermit delivers low-latency remote memory. For example, it reduces the 99th percentile latency of Memcached by 99.7% from 36 ms to 91 µs. Running Hermit over batch applications improves their overall throughput by 1.24× on average. These results are achieved without changing a single line of user code. 
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  4. Programmers often rely on online resources—such as code examples, documentation, blogs, and Q&A forums—to compare similar libraries and select the one most suitable for their own tasks and contexts. However, this comparison task is often done in an ad-hoc manner, which may result in suboptimal choices. Inspired by Analogical Learning and Variation Theory, we hypothesize that rendering many concept-annotated code examples from different libraries side-by-side can help programmers (1) develop a more comprehensive understanding of the libraries’ similarities and distinctions and (2) make more robust, appropriate library selections. We designed a novel interactive interface, ParaLib, and used it as a technical probe to explore to what extent many side-by-side concepted-annotated examples can facilitate the library comparison and selection process. A within-subjects user study with 20 programmers shows that, when using ParaLib, participants made more consistent, suitable library selections and provided more comprehensive summaries of libraries’ similarities and differences. 
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  5. Despite the trend of incorporating heterogeneity and specialization in hardware, the development of heterogeneous applications is limited to a handful of engineers with deep hardware expertise. We propose HeteroGen that takes C/C++ code as input and automatically generates an HLS version with test behavior preservation and better performance. Key to the success of HeteroGen is adapting the idea of search-based program repair to the heterogeneous computing domain, while addressing two technical challenges. First, the turn-around time of HLS compilation and simulation is much longer than the usual C/C++ compilation and execution time; therefore, HeteroGen applies pattern-oriented program edits guided by common fix patterns and their dependences. Second, behavior and performance checking requires testing, but test cases are often unavailable. Thus, HeteroGen auto-generates test inputs suitable for checking C to HLS-C conversion errors, while providing high branch coverage for the original C code. An evaluation of HeteroGen shows that it produces an HLS-compatible version for nine out of ten real-world heterogeneous applications fully automatically, applying up to 438 lines of edits to produce an HLS version 1.63x faster than the original version. 
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